Energy Modeling for KioskNet

We highlight the potential for accurate software-level energy modeling and prediction on commodity embedded computers for DTN deployments, such as KioskNet [3]. Our experiments confirm that accurate prediction of energy consumption, without relying on any hardware power measurement tool, is feasible. We performed controlled micro-benchmarks on the three major components of an embedded computing device: the processor, the storage, and the communication sub-systems. Energy consumption in all three follows simple models. 1. MOTIVATION KioskNet[3] uses low-cost commodity embedded computers as delay tolerant networking (DTN) [1] nodes to provide low-cost Internet connectivity for rural areas in developing regions. Electrical power is a major constraint in KioskNet target areas; therefore, computing in rural kiosks should be power-aware. Towards this goal we are interested in the answer of the following questions: 1. What is the energy consumption model in a typical kiosk computer? 2. How can the energy consumption during an opportunistic connection be minimized? 3. How can the energy consumption in kiosk computers be minimized during idle times? 2. EXPERIMENTS We performed controlled micro-benchmarks on the three major components of the embedded computers used in KioskNet: the processor, the storage, and the communication sub-systems. The early results suggest that energy consumption in such computers follows a simple model. Further evaluation of this model would help us understand power and performance [2] trade-offs of the DTN reference implementation, which is currently used in KioskNet. 4 5 6 7 8 9 10 11 12 13 0 20 40 60 80 100 P ow er ( W at t) CPU Activity (percent) 266 Mhz NSC SC1100 VIA Eden ESP 5000 Figure 1: Measured power vs. CPU activity for two different CPU types: 266 Mhz NSC SC1100 and VIA Eden ESP 5000. Figure 1 illustrates system power consumption vs. CPU activity. We used two different CPU types (266 Mhz NSC SC1100 and VIA Eden ESP 5000) for this experiment. Energy consumption on both of them follows a linear relation with CPU activity. PCPU = kcpu × x + Pbase Where Pbase includes the power consumption of all hardware components in the idle mode. Figure 2 presents the measured power consumption of the Atheros 802.11abg Wi-Fi card for different 802.11a data rates vs. network throughput. Surprisingly, power consumption of different 802.11a data rates are essentially identical and increase linearly with the network throughput. We intend to repeat this experiment for different wireless interfaces and different modes and data rates. Pnic = kmode × throughput + P0 P0 corresponds to the power consumption of the wireless interface when no data is being transmitted. This model for the wireless interface, although coarse grained, is sufficient for on-line software power prediction.